The processing of images is of particular relevance in the field of biology, where images of biological samples are to be analysed for the presence of certain features. The features may include intracellular components, fibres, granules. When using fluorescent microscopy, the distribution of single molecules labelled with a fluorophore can be imaged.
Biological images are generally processed using a segmentation method. Image segmentation is a form of image processing that distinguishes objects from their background in order to identify structures of interest. Image segmentation may be performed on the basis of characteristic object size and/or object shape. The size range may be known or modelled. For example, if the size of nuclei in a cell sample is within a known range, segmentation may be performed to identify features having the size of the nuclei. Other cellular objects of characteristic size include organelles such as mitochondria, cytoplasmic granules and vesicles within endo- and exocytic pathways. Segmentation on the basis of size may also be performed in order to detect whole cells or structures within cells.
Cell images are generally processed using the so-called top-hat based segmentation. Top-hat transforms are used to segment objects of a pre-defined size. Top-hat transforms run fast, provide good sensitivity to local details and have been proven useful for different fluorescent assays applications. A top-hat is, however, not selective to the shape of the local intensity landscape of an image. A top-hat is roughly equally sensitive to peak-like, ridge-like and edge-like structures having the cross-section for which the top-hat is optimized. This mixed sensitivity to different morphologies is the major source of under-detections, artifacts and false-positives in the analyses of granularity and fibre detection.
It is an object of the invention to provide a method for processing images having shape selectivity to peak-like structures.